Reduced-Reference Video Quality Assessment Using Discriminative Local Harmonic Strength With Motion Consideration

Gunawan, Irwan Prasetya; Ghanbari, M.

doi:10.1109/tcsvt.2007.913755

Cited by 59 publications

(31 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These techniques can be easily integrated into existing broadcasting systems, but generally lack in estimation accuracy. Specific NR methods have been proposed to estimate the distortion introduced by video coding [5,6] or to take in consideration the effects of channel losses [7]. We have recently proposed a NR system [8] for H.264/AVC video sequences which models the effect of temporal concealment to estimate the channelinduced distortion at the decoder; this method has been subsequently expanded in [9] and, since it acts as a substratum for the proposed quality assessment system, it will be briefly summarized in Section 2.2.…”

Section: Introductionmentioning

confidence: 99%

A reduced-reference video structural similarity metric based on no-reference estimation of channel-induced distortion

Albonico

Valenzise

Tagliasacchi

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

View full text Add to dashboard Cite

The reduced-reference (RR) approximation of a full-reference (FR) video quality assessment method is a convenient way to build evaluation metrics which are both intrinsically well correlated with human judgments and feasible to implement in a network scenario, without the need to explore the perceptual significance of new video features through mean opinion score tests. In this paper, we propose a RR approximation of the video structural similarity index (VSSIM), a FR metric which is known to be well descriptive of the video quality perceived by users. We focus on the visual degradation produced by channel transmission errors: first, at the encoder, a small set of salient structural video features is assembled and transmitted through the RR channel to the end-user; then, at the decoder the feature vector is combined with a fine-granularity, no-reference estimate of the channel-induced distortion to produce the VSSIM approximation. By uniformly quantizing the feature vector and compressing it using a context-adaptive, variable length encoder, we show that good correlation coefficients with ground-truth VSSIM (ρ = 0.85) may be achieved spending, respectively, less than 12 and 27 kbps for a video sequence with CIF or SD resolution.

show abstract

Section: Introductionmentioning

confidence: 99%

A reduced-reference video structural similarity metric based on no-reference estimation of channel-induced distortion

Albonico

Valenzise

Tagliasacchi

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

View full text Add to dashboard Cite

show abstract

“…Gunawan and Ghanbari have developed a RR method that uses local harmonic strength features from the original video to calculate the amount of impairments or quality of the affected video [51]. Harmonic gains and loss correlate well with two very common types of impairment present in MPEG encoded video, i.e.…”

Section: Reduced and No Reference Methodsmentioning

confidence: 99%

Computational Inference and Control of Quality in Multimedia Services

Menkovski

2015

Springer Theses

View full text Add to dashboard Cite

Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:www.tue.nl/taverne Take down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nl providing details and we will investigate your claim. Copyright c 2013 by Vlado Menkovski All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the prior written consent of the author. Typeset using L A T E X, printed in The NetherlandsTo my parents, Angel who dedicated his life to his sons and Snežana whose strength inspires me deeply. SummaryQuality is the degree of excellence we expect of a service or a product. It is also one of the key factors that determine its value. For multimedia services, understanding the experienced quality means understanding how the delivered fidelity, precision and reliability correspond to the users' expectations. Yet the quality of multimedia services is inextricably linked to the underlying technology. It is developments in video recording, compression and transport as well as display technologies that enables high quality multimedia services to become ubiquitous. The constant evolution of these technologies delivers a steady increase in performance, but also a growing level of complexity. As new technologies stack on top of each other the interactions between them and their components become more intricate and obscure. In this environment optimizing the delivered quality of multimedia services becomes increasingly challenging. The factors that affect the experienced quality, or Quality of Experience (QoE), tend to have complex non-linear relat...

show abstract

“…Ma et al in [12] developed a technique based on the statistical modeling of the Discrete Cosine Transform (DCT) coefficient distributions, exploiting the identical nature of the distributions between adjacent subbands and the coefficients into a three-level tree using the Generalized Gaussian Density (GGD) function. Concerning the evaluation of video, Gunawan and Ghanbari [13], [14] described a method where a discriminative analysis of the harmonic strength computed from the edge-detected frame is employed to create harmonics of gain and loss information. Hewage and Martini [15] proposed a metric for 3D video (encoded in H.264/AVC format), which uses PSNR and is based on the edge detection.…”

Section: Introductionmentioning

confidence: 99%

A Reduced-Reference Video Quality Assessment Method Based on the Activity-Difference of DCT Coefficients

Silva

Fonseca

Pohl

2013

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYA simple and efficient reduced-reference video quality assessment method based on the activity-difference of DCT coefficients is proposed. The method provides better accuracy, monotonicity, and consistent predictions than the PSNR full-reference metric and comparable results with the full-reference SSIM. It also shows an improved performance to a similar VQ technique based on the calculation of the pixel luminance differences performed in the spatial-domain. key words: DCT, reduced-reference metric, video quality assessment

show abstract

Reduced-Reference Video Quality Assessment Using Discriminative Local Harmonic Strength With Motion Consideration

Cited by 59 publications

References 31 publications

A reduced-reference video structural similarity metric based on no-reference estimation of channel-induced distortion

A reduced-reference video structural similarity metric based on no-reference estimation of channel-induced distortion

Computational Inference and Control of Quality in Multimedia Services

A Reduced-Reference Video Quality Assessment Method Based on the Activity-Difference of DCT Coefficients

Contact Info

Product

Resources

About